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PyTorch Logistic Regression ~ MLP model

PyTorch Logistic Regression ~ MLP model

  1. logistic_regression.py - Using torch.nn module, analysing sklearn DIGITS dataset
  2. logistic_regression_low.py - NOT using torch.nn module, analysing sklearn DIGITS dataset
  3. mnist_mlp.py
# -*- coding: utf-8 -*-
#
# logistic_regression.py
# date. 8/22/2017
#
import numpy as np
import torch
from torch.autograd import Variable
import torch.nn as nn
import torch.nn.functional as F
from sklearn.datasets import load_digits
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, confusion_matrix
# CPU演算とGPU演算を切り換えるスイッチ.GPU演算では,CPU-GPU間のメモリ・コピーが行われる.
if torch.cuda.is_available():
GPU_sw = True
else:
GPU_sw = False
print('GPU_sw = ', GPU_sw)
def load_data():
digits = load_digits()
y = digits.target
n_samples = len(digits.images)
X = digits.images.reshape((n_samples, -1))
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.2, random_state=0)
return X_train, X_test, y_train, y_test
class Net(nn.Module):
# torch.nn modeling
def __init__(self, n_feature, n_class):
super(Net, self).__init__()
self.fc = nn.Linear(n_feature, n_class)
def forward(self, x):
y = self.fc(x)
return y
def train_feed(X_train, y_train, idx, batch_size):
# Training phase におけるデータ供給
dtype = torch.FloatTensor
n_samples = X_train.shape[0]
if idx == 0:
perm = np.random.permutation(n_samples)
X_train = X_train[perm]
y_train = y_train[perm]
start = idx
end = start + batch_size
if end > n_samples:
perm = np.random.permutation(n_samples)
X_train = X_train[perm]
y_train = y_train[perm]
start = 0
end = start + batch_size
batch_x = np.asarray(X_train[start:end], dtype=np.float32)
batch_x = batch_x / 16.0 # image のスケーリング
batch_y = np.asarray(y_train[start:end], dtype=np.int64)
idx = end
if GPU_sw:
var_x = Variable(torch.from_numpy(batch_x).cuda())
var_y = Variable(torch.from_numpy(batch_y).cuda())
else:
var_x = Variable(torch.from_numpy(batch_x))
var_y = Variable(torch.from_numpy(batch_y))
return var_x, var_y, idx
if __name__ == '__main__':
# Load Data
X_train, X_test, y_train, y_test = load_data()
n_feature = 64
n_class = 10
batch_size = 100
# define network
if GPU_sw:
net = Net(n_feature, n_class).cuda()
else:
net = Net(n_feature, n_class)
loss_fn = torch.nn.CrossEntropyLoss() # 損失関数の定義
optimizer = torch.optim.SGD(net.parameters(),
lr=0.003, momentum=0.9) # オプティマイザ
print('Training...')
train_index = 0
for t in range(10000):
batch_x, batch_y, train_index = train_feed(X_train, y_train,
train_index, batch_size)
y_pred = net.forward(batch_x)
loss = loss_fn(y_pred, batch_y)
if t % 1000 == 0:
print('{:>5d}: loss = {:>10.3f}'.format(t, loss.data[0]))
# zero the gradient buffers, 勾配gradを初期化(ゼロ化)する.
optimizer.zero_grad()
# Backward pass: 誤差の逆伝搬を行って,パラメータの変化量を算出する.
loss.backward()
# パラメータ更新
optimizer.step()
# Test process
X_test = np.asarray(X_test, dtype=np.float32) / 16.0 # imageのスケーリング
if GPU_sw:
var_x_te = Variable(torch.from_numpy(X_test).cuda())
y_pred_te = net.forward(var_x_te)
y_pred_proba = y_pred_te.data.cpu().numpy()
else:
var_x_te = Variable(torch.from_numpy(X_test))
y_pred_te = net.forward(var_x_te)
y_pred_proba = y_pred_te.data.numpy()
y_pred_ = np.argmax(y_pred_proba, axis=1)
# テスト結果の評価
confmat = confusion_matrix(y_test, y_pred_)
print('\nconfusion matrix:')
print(confmat)
accu = accuracy_score(y_test, y_pred_)
print('\naccyracy = {:>.4f}'.format(accu))
# -*- coding: utf-8 -*-
#
# logistic_regression_low.py
# date. 8/22/2017
#
import numpy as np
import torch
from torch.autograd import Variable
from sklearn.datasets import load_digits
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, confusion_matrix
# CPU演算とGPU演算を切り換えるスイッチ.GPU演算では,CPU-GPU間のメモリ・コピーが行われる.
if torch.cuda.is_available():
GPU_sw = True
else:
GPU_sw = False
print('GPU_sw = ', GPU_sw)
def load_data():
digits = load_digits()
y = digits.target
n_samples = len(digits.images)
X = digits.images.reshape((n_samples, -1))
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.2, random_state=0)
return X_train, X_test, y_train, y_test
class Model:
# torch.nn APIを用いないモデル構築
def __init__(self, n_feature, n_class):
if GPU_sw:
dtype = torch.cuda.FloatTensor
else:
dtype = torch.FloatTensor
self.W = Variable((torch.randn(n_feature, n_class) * 0.01).type(dtype),
requires_grad=True)
self.b = Variable(torch.zeros(n_class).type(dtype), requires_grad=True)
def forward(self, x):
"""
calculate network forwarding prop.
return logits before activation (LogSoftMax)
"""
y = torch.mm(x, self.W) + self.b # using "Broadcasting" for "b"
return y
@property
def params(self):
return [self.W, self.b]
def train_feed(X_train, y_train, idx, batch_size):
# Training phase におけるデータ供給
dtype = torch.FloatTensor
n_samples = X_train.shape[0]
if idx == 0:
perm = np.random.permutation(n_samples)
X_train = X_train[perm]
y_train = y_train[perm]
start = idx
end = start + batch_size
if end > n_samples:
perm = np.random.permutation(n_samples)
X_train = X_train[perm]
y_train = y_train[perm]
start = 0
end = start + batch_size
batch_x = np.asarray(X_train[start:end], dtype=np.float32)
batch_x = batch_x / 16.0 # image のスケーリング
batch_y = np.asarray(y_train[start:end], dtype=np.int64)
idx = end
if GPU_sw:
var_x = Variable(torch.from_numpy(batch_x).cuda())
var_y = Variable(torch.from_numpy(batch_y).cuda())
else:
var_x = Variable(torch.from_numpy(batch_x))
var_y = Variable(torch.from_numpy(batch_y))
return var_x, var_y, idx
if __name__ == '__main__':
# Load Data
X_train, X_test, y_train, y_test = load_data()
n_feature = 64
n_class = 10
batch_size = 100
# define network
model = Model(n_feature, n_class)
loss_fn = torch.nn.CrossEntropyLoss() # 損失関数の定義
optimizer = torch.optim.SGD(model.params,
lr=0.003, momentum=0.9) # オプティマイザ
print('Training...')
train_index = 0
for t in range(10000):
batch_x, batch_y, train_index = train_feed(X_train, y_train,
train_index, batch_size)
y_pred = model.forward(batch_x)
loss = loss_fn(y_pred, batch_y)
if t % 1000 == 0:
print('{:>5d}: loss = {:>10.3f}'.format(t, loss.data[0]))
# 勾配gradを初期化(ゼロ化)
optimizer.zero_grad()
# Backward pass: 誤差の逆伝搬を行って,パラメータの変化量を算出する.
loss.backward()
# パラメータ更新
optimizer.step()
# Test process
X_test = np.asarray(X_test, dtype=np.float32) / 16.0 # imageのスケーリング
if GPU_sw:
var_x_te = Variable(torch.from_numpy(X_test).cuda())
y_pred_te = model.forward(var_x_te)
y_pred_proba = y_pred_te.data.cpu().numpy()
else:
var_x_te = Variable(torch.from_numpy(X_test))
y_pred_te = model.forward(var_x_te)
y_pred_proba = y_pred_te.data.numpy()
y_pred_ = np.argmax(y_pred_proba, axis=1)
# テスト結果の評価
confmat = confusion_matrix(y_test, y_pred_)
print('\nconfusion matrix:')
print(confmat)
accu = accuracy_score(y_test, y_pred_)
print('\naccyracy = {:>.4f}'.format(accu))
# -*- coding: utf-8 -*-
#
# mnist_mlp.py
# date. 8/24/2017
# library: torch v0.2.0
#
import numpy as np
import torch
from torch.autograd import Variable
import torch.nn as nn
import torch.nn.functional as F
from torchvision import datasets as dset
from torchvision import transforms
from sklearn.metrics import accuracy_score, confusion_matrix
# CPU演算とGPU演算を切り換えるスイッチ.GPU演算では,CPU-GPU間のメモリ・コピーが行われる.
GPU_sw = torch.cuda.is_available()
print('GPU_sw = ', GPU_sw)
def mk_data_loader(dirn, batch_size, train=True, gpu_sw=False):
"""
MNIST data loader 関数
"""
kwargs = {'num_workers': 1, 'pin_memory': True} if gpu_sw else {}
data_loader = torch.utils.data.DataLoader(
dset.MNIST(dirn, train=train, download=False,
transform=transforms.Compose([
transforms.ToTensor(),
# torchvision.transforms.Normalize(mean, std)
transforms.Normalize((0.1307,), (0.3081,))
])),
batch_size=batch_size, shuffle=True, **kwargs)
return data_loader
class Net(nn.Module):
"""
torch.nn modeling - 3層 MLP model
"""
def __init__(self, n_feature, n_class, n_hidden1=512, n_hidden2=256):
super(Net, self).__init__()
self.fc1 = nn.Linear(n_feature, n_hidden1)
self.fc2 = nn.Linear(n_hidden1, n_hidden2)
self.fc3 = nn.Linear(n_hidden2, n_class)
def forward(self, x):
net = F.relu(self.fc1(x))
net = F.relu(self.fc2(net))
y = self.fc3(net)
return y
if __name__ == '__main__':
# Data Loader
batch_size = 100
train_loader = mk_data_loader('../MNIST_data', batch_size,
train=True, gpu_sw=GPU_sw)
test_loader = mk_data_loader('../MNIST_data', batch_size,
train=False, gpu_sw=GPU_sw)
# define network
n_feature = 784
n_class = 10
if GPU_sw:
net = Net(n_feature, n_class).cuda()
else:
net = Net(n_feature, n_class)
loss_fn = torch.nn.CrossEntropyLoss() # 損失関数の定義
optimizer = torch.optim.SGD(net.parameters(),
lr=0.003, momentum=0.9) # オプティマイザ
# Train プロセス
print('Training...')
n_epochs = 10
for epoch in range(1, n_epochs+1):
for i, (data, target) in enumerate(train_loader):
data = data.resize_([batch_size, n_feature])
if GPU_sw:
data, target = data.cuda(), target.cuda()
data, target = Variable(data), Variable(target)
y_pred = net.forward(data)
loss = loss_fn(y_pred, target)
if i % 100 == 0:
print('epoch {:>3d}:{:>5d}: loss = {:>10.3f}'.format(
epoch, i, loss.data[0]))
# zero the gradient buffers, 勾配gradを初期化(ゼロ化)する.
optimizer.zero_grad()
# Backward pass: 誤差の逆伝搬を行って,パラメータの変化量を算出する.
loss.backward()
# パラメータ更新
optimizer.step()
# Test プロセス
y_pred = []
y_target = []
for data, target in test_loader:
data = data.resize_([batch_size, n_feature])
if GPU_sw:
data, target = data.cuda(), target.cuda()
data, target = Variable(data), Variable(target)
y_pred_ = net.forward(data)
if GPU_sw:
y_pred.extend(y_pred_.data.cpu().numpy())
y_target.extend(target.data.cpu().numpy())
else:
y_pred.extend(y_pred_.data.numpy())
y_target.extend(target.data.numpy())
y_pred_am = np.argmax(np.asarray(y_pred), axis=1)
y_target = np.asarray(y_target)
# テスト結果の評価
confmat = confusion_matrix(y_target, y_pred_am)
print('\nconfusion matrix:')
print(confmat)
accu = accuracy_score(y_target, y_pred_am)
print('\naccyracy = {:>.4f}\n'.format(accu))
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